Vol 8 No 4 (2024)
Dublin Core
Title
Vol 8 No 4 (2024)
Collection Items
Improved Backpropagation Using Genetic Algorithm for Prediction of Anomalies and Data Unavailability
Anomalies and data unavailability are significant challenges in conducting surveys, affecting the validity, reliability, and accuracy of analysis results. Various methods address these issues, including the Backpropagation Neural Network…
Quantum Perceptron: A New Approach for Predicting Rice Prices at the Indonesian Wholesale Trade Leve
The wholesale rice trade in Indonesia encounters various challenges in forecasting prices. These challenges are influenced by factors such as weather, government policies, global market conditions, and other economic variables. Accurate…
Classification ofToraja Wood Carving Motif Images Using Convolutional Neural Network (CNN)
Wood carving is a cultural heritage with deep meaning and significance for the Toraja ethnic group's culture. By understanding the meaning of each Toraja carving, both tourists and the local community can gain knowledge about Toraja culture, thereby…
Twitter Sentiment Analysis TowardsCandidates ofthe 2024 Indonesian Presidential Election
Indonesia will hold general elections in 2024. Long before the elections were held, the topic related to elections was widely discussed on news portals and social media, including Twitter. A fewstudies related to Indonesian election have tried to…
waste classification; transfer learning; EfficientNet-B0
waste classification; transfer learning; EfficientNet-B0
Comparing Correlation-Based Feature Selection and Symmetrical Uncertainty for Student Dropout Prediction
Predicting student dropout is essential for universities dealing with high attrition rates.This study compares two feature selection (FS) methods—correlation-based feature selection (CFS) and symmetrical uncertainty (SU)—in …
Increasing the Accuracy of Brain Stroke Classification using Random Forest Algorithm with Mutual Information Feature Selection
Brain stroke stands out as a leading cause of death, distinguishing it from common illnesses and highlighting the critical need to utilize machine learning techniques to identify symptoms. Among these techniques, the Random Forest (RF) …
Advanced Earthquake Magnitude Prediction Using Regression and Convolutional Recurrent Neural Networks
Earthquake magnitude prediction is critical in seismology, with significant implications for disaster risk management and mitigation. This study presents a novel earthquake magnitude prediction model by integrating regression …
Analysis of Sulawesi Earthquake Data from 2019 to 2023 using DBSCAN Clustering
Sulawesi is a region in Indonesia known for its significant seismic activity, and its history of impactful earthquakes makes it an area of crucial importance for in-depth analysis. This study analyses earthquake occurrence data in the Sulawesi region…
Comparative Analysis of Gradient Descent Learning Algorithms in Artificial Neural Networks for Forecasting Indonesian Rice Prices
Artificial Neural Networks (ANN) are a field of computer science that mimics the way the human brain processes data. ANNs can be used to classify, estimate, predict, or simulate new data from similar sources. The commonly used …